Most content recommendation systems may be classified as data filtering systems which predict an “interest” that a user would have in content, such as articles, news, music, books, movies, and videos. These content recommendation systems usually attempt to model the characteristics of the user and/or the content related thereto and the user's behaviors post consumption of the content. Most content recommendation systems usually generate recommendations of content in one of two ways: a) collaborative filtering, and b) content-based filtering. In collaborative filtering, the content recommendation systems attempts to generate a model from a user's past behaviors, as well as decisions made by other users having similar characteristics, and use that model to predict other content that the user may be interested in. In content-based filtering approaches, the content recommendation systems utilize a series of discrete characteristics of known content, which the user has consumed, in order to recommend additional content with similar properties.
The prediction accuracy of the traditional recommendation systems is highly dependent on the volume of user's past behavioral data that the recommendation system has access to. For example, in order to predict the type or genre of content that a user would be interested in, the content recommendation systems monitor and collect as much of the user's past activities and related content as possible. However, if the user is new to a recommendation system, it would be very difficult for the recommendation system to obtain enough past behavioral data of the new user in order to make any meaningful recommendation. Furthermore, most content recommendation systems usually only acquire past behavioral data voluntarily provided by users, for example through questionnaires and surveys, or behavioral data recorded by the content recommendation systems when users are directly interacting with the content recommendation systems, for example cookies or activity logs associated with the user. As a result, inactive users of the recommendation systems cannot be used to provide data for building recommendation models. Thus, for new users or inactive users, the traditional content recommendation systems become less effective in personalized content recommendation.